CN104538980A - Self-balanced quick load-reducing control method for microgrid - Google Patents

Abstract

The invention discloses a self-balanced quick load-reducing control method for a microgrid. The method comprises the following steps: determining an equivalent inertia constant of the microgrid, calculating a power balance of the microgrid and evaluating the disturbance amplitude in the microgrid; lowering the changing speed of frequency respectively through frequency responsive active power control of an energy storage system and droop control of a wind turbine; and balancing the initial power vacancy in the microgrid by virtue of rotary kinetic energy, a load adjusting effect and a distributive power supply adjusting capacity, calculating power to be reduced according to the power balance, and obtaining the dynamic characteristics of the system frequency when no spinning reserve is available according to the requirement on load reducing time under various power vacancies. According to the method disclosed by the invention, when the microgrid operates independently and relatively great disturbance occurs in the microgrid, self-balanced load reduction is implemented to eliminate power unbalance in the microgrid by calculating the equivalent inertia constant of the microgrid and evaluating the disturbance amplitude in the microgrid on line.

Description

A kind of micro-capacitance sensor self-balancing fast cutback control method

Technical field

The present invention relates to a kind of micro-capacitance sensor self-balancing fast cutback control method.

Background technology

Micro-capacitance sensor comprises wind power generation, photovoltaic generation distributed power supply, energy storage and load.Exerting oneself of distributed power source has intermittence and randomness, and in grid-connected situation, major network can provide power to support for micro-capacitance sensor, and each distributed power source all can run on maximal power tracing point; And during from network operation, due to the fluctuation of the unbalanced power in micro-capacitance sensor and load, need to take certain control measure to ensure the power-balance of micro-capacitance sensor inside, the frequency and voltage maintaining micro-capacitance sensor inside is stablized.

Micro-capacitance sensor generally takes hierarchical control mode, in each distributed power source outlet, sub-controller is installed, gather the power output of distributed power source outlet, frequency and frequency change rate, and micro-grid master control device is installed at site place, the data uploaded by fast messaging network reception sub-controller.Micro-grid master control device, to the data analysis uploaded and gather with after calculating, controls the power-balance in micro-capacitance sensor and frequency stabilization.

Conventional micro-capacitance sensor internal power balance control measure have off-load, cut machine, generator change fast exert oneself, switching shunt reactor and shunt capacitor etc.Current research focuses mostly on off-load strategy, mainly contains UFLS, by frequency change rate off-load and chain cutting load etc.

UFLS adopts the power shortage of the method estimating system of " Approach by inchmeal ", disconnects corresponding load according to each operating frequency of taking turns.During emergent power vacancy, must negatively wait for below frequency decrease to operating value, also need certain time-delay for preventing from cutting by mistake.And the capacity of micro-capacitance sensor is little, power shortage ratio is large, rotary inertia is little, the decrease speed of voltage to frequency is very fast, and traditional UFLS bradypragia, often causes micro-capacitance sensor frequency collapse.

Then too sensitive by frequency change rate (ROCOF) off-load, system be disturbed or short trouble time easy malfunction.By the impact of system inertia change, frequency and ROCOF certainty of measurement, the cutting load amount of calculating has larger error.These errors of calculation, once exceed the regulating power of micro-capacitance sensor weakness, will cause frequency keeps to decline.

Interlocking cutting load to the inhibition of frequency decrease clearly, but due to the peak valley change of generated output and load inconsistent, micro-capacitance sensor internal power vacancy is changed greatly, easily caused and cut or owe to cut.And the operational mode of distributed power source is more flexible in micro-capacitance sensor, may form " power surplus type ", " power shortage type " and " self-sufficient type " etc. after disconnecting with major network dissimilar, fixing connection cutting load amount is obviously difficult to adapt to various operational mode.

Summary of the invention

The present invention is in order to solve the problem, propose a kind of micro-capacitance sensor self-balancing fast cutback control method, this method at micro-capacitance sensor independent operating and occur in micro-capacitance sensor comparatively large disturbances time, by line computation micro-capacitance sensor equivalent inertia constant and estimate the perturbation amplitude of micro-capacitance sensor, enforcement self-balancing load shedding eliminates the unbalanced power in micro-capacitance sensor, utilize droop characteristic to alleviate the Rapid Variable Design of frequency simultaneously, thus make micro-capacitance sensor quickly recover to steady operational status.

To achieve these goals, the present invention adopts following technical scheme:

A kind of micro-capacitance sensor self-balancing fast cutback control method, comprises the following steps:

(1) determine the equivalent inertia constant of micro-capacitance sensor, calculate the power difference of micro-capacitance sensor, the perturbation amplitude in micro-capacitance sensor is estimated;

(2) pace of change of frequency is slowed down respectively by the frequency response real power control of energy-storage system and the droop control of wind turbine;

(3) utilize rotation function, adjustment effect of load and distributed power source regulating power to balance the initial power vacancy in micro-capacitance sensor, according to power difference, calculate the power needing off-load, by under various power shortage to the requirement of off-load time, system frequency dynamic characteristic when obtaining for subsequent use without spin.

In described step (1), determine that the method for the equivalent inertia constant of micro-capacitance sensor is:

In the power distribution network comprising mixing generation of electricity by new energy, inertia constant H is defined by following formula:

But to dissimilar generator, the definition of inertia constant is different; The H of synchronous generator _sG

Wherein, J is the moment of inertia kgm of rotor ², ω _mthe rated speed rad/s of rotor,

For constant speed deliberate speed loss wind field:

J is the moment of inertia of every Fans, and N is the blower fan number of units put into operation; ω _miit is the speed of service of blower fan i.

In described step (1), when wind energy turbine set, photovoltaic plant and energy storage device are by back-to-back converters connecting system, they are 0 to the equivalent inertia constant of systematic contributions, if but addition of the blower fan of virtual inertia simulation link, photovoltaic plant and energy storage device, then its equivalent inertia constant non-vanishing; Now need the equivalent inertia constant of On-line Estimation distributed power source.

In described step (1), the sub-controller installed by the outlet of each distributed power source gathers power and the frequency change rate of distributed power source outlet, and estimate the equivalent inertia constant of this distributed power source, the data window length that order calculates is N ₁* t _s, wherein N ₁for sampled point number, t _sfor the sampling interval; Data window interval length gets N ₂* t _s, then the inertia estimated is:

${2H}_{\mathrm{eq}}=-\frac{{\stackrel{\‾}{P}}_{\mathrm{pos}}-{\stackrel{\‾}{P}}_{\mathrm{pre}}}{\stackrel{\‾}{R}{\stackrel{\‾}{F}}_{\mathrm{pos}}-\stackrel{\‾}{R}{\stackrel{\‾}{F}}_{\mathrm{pre}}}---\left(4\right)$

${\stackrel{\‾}{P}}_{\mathrm{pre}}=\frac{1}{N_1}\underset{i=1}{\overset{N_1}{\mathrm{\Σ}}}P\left(i\right),{\stackrel{\‾}{P}}_{\mathrm{pos}}=\frac{1}{N_1}\underset{i=N_1+N_2+1}{\overset{2N_1+N_2}{\mathrm{\Σ}}}P\left(i\right)$

$\stackrel{\‾}{R}{\stackrel{\‾}{F}}_{\mathrm{pre}}=\frac{1}{N_1}\underset{i=1}{\overset{N_1}{\mathrm{\Σ}}}\frac{\mathrm{df}}{\mathrm{dt}}\left(i\right),\stackrel{\‾}{R}{\stackrel{\‾}{F}}_{\mathrm{pos}}=\frac{1}{N_1}\underset{i=N_1+N_2+1}{\overset{2N_1+N_2}{\mathrm{\Σ}}}\frac{\mathrm{df}}{\mathrm{dt}}\left(i\right)$

Wherein, P (i) and for distributed power source exports power and the frequency change rate sampled value of i-th sampled point, N ₁and N ₂span is 3-5; Disturbance each time in micro-capacitance sensor, sub-controller all will estimate the equivalent inertia constant of distributed power source, and this estimated value is uploaded to micro-grid master control device.

In described step (1), the perturbation amplitude method of estimation in micro-capacitance sensor is: the inertia constant H establishing every platform generator _i, record frequency change rate at generator port obtain the imbalance power Δ p of generator port _i, i.e. generator mechanical power p _miwith electromagnetic power p _eibetween imbalance power:

$\frac{2H_i}{f_n}\frac{{\mathrm{df}}_i}{\mathrm{dt}}=p_{\mathrm{mi}}-p_{\mathrm{ei}}=\mathrm{\Δ}{p}_i---\left(5\right)$

Wherein, f _nfor electrical network rated frequency, in the system with N platform generator, the imbalance power between the power that all generators send power and all load consumption is expressed as:

$\mathrm{\Δ}{p}_{\mathrm{total}}={\mathrm{\Σ}}_{i=1}^N\mathrm{\Δ}{p}_i={\mathrm{\Σ}}_{i=1}^N\frac{2H_i}{f_n}\frac{{\mathrm{df}}_i}{\mathrm{dt}}=\frac{2H_{\mathrm{ec}}}{f_n}\frac{{\mathrm{df}}_{\mathrm{ec}}}{\mathrm{dt}}---\left(6\right)$

Wherein,

$H_{\mathrm{ec}}=\underset{i=1}{\overset{N}{\mathrm{\Σ}}}{H}_i$

$f_{\mathrm{ec}}=\underset{i=1}{\overset{N}{\mathrm{\Σ}}}{H}_i{f}_i/\underset{i=1}{\overset{N}{\mathrm{\Σ}}}{H}_i$

H _ecand f _ecthe inertia centre frequency of equivalent inertia constant and equivalence respectively.

In described step (1), in micro-capacitance sensor, ignore the frequency f at computing system equivalent inertia center _ec, directly calculate the frequency f of micro-grid connection point bus _meas, the perturbation amplitude simplified in power distribution network is estimated as:

$\mathrm{\Δ}{P}_{\mathrm{total}}=\frac{2H_{\mathrm{ec}}}{f_n}\·\frac{{\mathrm{df}}_{\mathrm{meas}}}{\mathrm{dt}}=\frac{2}{f_n}(H_{\mathrm{SG}}+H_{\mathrm{WF}}+H_{\mathrm{PV}}+H_{\mathrm{battery}})\frac{{\mathrm{df}}_{\mathrm{meas}}}{\mathrm{dt}}---\left(8\right).$

Wherein, H _pVand H _batterybe respectively the equivalent inertia constant of photovoltaic plant and battery.

In described step (2), the frequency response real power control of energy-storage system, namely droop control method is: use system frequency as input signal, when system frequency changes, battery changes power stage dynamically.

In described step (2), the droop control method of wind turbine is: using system frequency as input signal, when system frequency changes, and the reference moment dynamic change thereupon of wind turbine.

In described step (3), by step 1) computing system power difference, then calculate the power needing off-load,

$P_{\mathrm{shedding}}=\frac{{2H}_{\mathrm{ec}}}{f_n}\frac{{\mathrm{df}}_{\mathrm{meas}}}{\mathrm{dt}}-\mathrm{k\φ}---\left(10\right)$

Be wherein adjustment factor, span is 0 ~ 1;

Wherein, the Load Regulation ability φ of micro-capacitance sensor:

φ＝ΔP _G-ΔP _L＝-K _GΔfP _Ge-K _LΔfP _Le＝-K _SΔfP _Ge(9)

Δ f--micro-capacitance sensor stablize after frequency departure, relevant with the adjustable range of primary frequency modulation;

K _g--the power versus frequency characteristic coefficient of generator;

K _l--LOAD FREQUENCY mediating effect+6 coefficient;

P _ge, P _le--the rated generation power of micro-capacitance sensor and rated load power.

In described step (3), for reducing cutting load amount, generator regulating power and adjustment effect of load should be taken into full account, the value improved as far as possible; And for making micro-capacitance sensor return to rapidly rated frequency, then need cutting load amount as far as possible close to power shortage, the value namely reduced; As k=1, system still can rest on low frequency state the long period, and recovery process is slow; During k=0, system will return to rated frequency very soon; Rational adjustment factor k is determined according to the type of generator and governing system response characteristic.

In described step (3), in order to unnecessary load can be cut in time, stop the continuous decrease of frequency, need to obtain the requirement to the off-load time under various power shortage; System frequency dynamic characteristic when obtaining for subsequent use without spin, when deriving different capacity vacancy, frequency drops to the time of certain certain value, namely maximum off-load moment-power shortage (t-dP) curve:

$t_{\max }=-\frac{H}{K_L}\ln (1-\frac{\mathrm{\Δf}{K}_L}{f_n\mathrm{\ΔP}})---\left(11\right).$

According to the t calculated _max, determine the round n of off-load, each off-load amount is Δ P _i:

$\mathrm{\Δ}{P}_i=K_i\mathrm{\Δ}{P}_{\mathrm{total}}=K_i\frac{{2H}_{\mathrm{ec}}}{f_n}\·\frac{{\mathrm{df}}_{\mathrm{meas}}}{\mathrm{dt}}---\left(12\right);$

$\underset{i=1}{\overset{n}{\mathrm{\Σ}}}{K}_i=1---\left(13\right);$

Complete adjustment required time:

T＝n*t _a≤t _max(14)。

Beneficial effect of the present invention is:

(1) at micro-capacitance sensor independent operating and occur in micro-capacitance sensor comparatively large disturbances time, by line computation micro-capacitance sensor equivalent inertia constant and estimate the perturbation amplitude of micro-capacitance sensor, implement self-balancing load shedding and eliminate unbalanced power in micro-capacitance sensor;

(2) utilize droop characteristic to alleviate the Rapid Variable Design of frequency, make micro-capacitance sensor quickly recover to steady operational status.

Accompanying drawing explanation

Fig. 1 is the schematic diagram of typical micro-capacitance sensor;

Fig. 2 is the structural representation of energy storage (battery);

Fig. 3 is the real power control block diagram of energy-storage system;

Fig. 4 is the idle control block diagram of energy-storage system;

Fig. 5 is the droop control figure of energy-storage system;

Fig. 6 is the Torque Control block diagram using droop control;

Fig. 7 is that micro-capacitance sensor simplifies frequency model figure;

Fig. 8 be maximum off-load moment-power shortage curve (t-dP) schematic diagram;

Fig. 9 is UFLS and real-time load shedding schematic diagram.

Embodiment:

Below in conjunction with accompanying drawing and embodiment, the invention will be further described.

Fig. 1 is the schematic diagram of typical micro-capacitance sensor, comprises small power station's unit, gas turbine, wind energy turbine set, photovoltaic DC field, energy-storage battery and sub-load etc.

1. the perturbation amplitude of On-line Estimation micro-capacitance sensor

First stage estimates the perturbation amplitude in micro-capacitance sensor, comprises two important steps, and one is the equivalent inertia constant determining micro-capacitance sensor, and another is the power difference calculating micro-capacitance sensor.

In the power distribution network comprising mixing generation of electricity by new energy, inertia constant H can be defined by following formula:

But to dissimilar generator, the definition of inertia constant is different.The H of synchronous generator _sG

Wherein, J is the moment of inertia kgm of rotor ², ω _mthe rated speed rad/s. of rotor

Constant speed deliberate speed loss wind field

J is the moment of inertia of every Fans, and N is the blower fan number of units put into operation.ω _miit is the speed of service of blower fan i.

When wind energy turbine set, photovoltaic plant and energy storage device (battery) are by back-to-back converters connecting system, they are 0 to the equivalent inertia constant of systematic contributions, because by the decoupling zero of power electronics interface between himself inertia and electrical network.If but addition of the blower fan of virtual inertia simulation link, photovoltaic plant and energy storage device, then its equivalent inertia constant non-vanishing.Now just need the equivalent inertia constant of On-line Estimation distributed power source.

The sub-controller that the outlet of each distributed power source is installed gathers power and the frequency change rate of distributed power source outlet, and can estimate the equivalent inertia constant of this distributed power source, the data window length that order calculates is N ₁* t _s, wherein N ₁for sampled point number, t _sfor the sampling interval; Data window interval length gets N ₂* t _s, then the inertia estimated is:

${2H}_{\mathrm{eq}}=-\frac{{\stackrel{\‾}{P}}_{\mathrm{pos}}-{\stackrel{\‾}{P}}_{\mathrm{pre}}}{\stackrel{\‾}{R}{\stackrel{\‾}{F}}_{\mathrm{pos}}{-\stackrel{\‾}{\mathrm{RF}}}_{\mathrm{pre}}}---\left(4\right)$

${\stackrel{\‾}{P}}_{\mathrm{pre}}=\frac{1}{N_1}\underset{i=1}{\overset{N_1}{\mathrm{\Σ}}}P\left(i\right),{\stackrel{\‾}{P}}_{\mathrm{pos}}=\frac{1}{N_1}\underset{i=N_1+N_2+1}{\overset{2N_1+N_2}{\mathrm{\Σ}}}P\left(i\right)$

$\stackrel{\‾}{R}{\stackrel{\‾}{F}}_{\mathrm{pre}}=\frac{1}{N_1}\underset{i=1}{\overset{N_1}{\mathrm{\Σ}}}\frac{\mathrm{df}}{\mathrm{dt}}\left(i\right),\stackrel{\‾}{R}{\stackrel{\‾}{F}}_{\mathrm{pos}}=\frac{1}{N_1}\underset{i=N_1+N_2+1}{\overset{2N_1+N_2}{\mathrm{\Σ}}}\frac{\mathrm{df}}{\mathrm{dt}}\left(i\right)$

Wherein, N ₁and N ₂span is generally 3-5.Disturbance each time in micro-capacitance sensor, sub-controller all will estimate the equivalent inertia constant of distributed power source, and this estimated value is uploaded to micro-grid master control device.After master controller receives data, for the estimation of perturbation amplitude.

Perturbation amplitude in electric power system can be obtained by following analysis: the inertia constant H of known every platform generator _i, record frequency change rate at generator port just can obtain the imbalance power Δ p of generator port _i, i.e. generator mechanical power p _miwith electromagnetic power p _eibetween imbalance power.

$\frac{2H_i}{f_n}\frac{{\mathrm{df}}_i}{\mathrm{dt}}=p_{\mathrm{mi}}-p_{\mathrm{ei}}=\mathrm{\Δ}{p}_i---\left(5\right)$

In the system with N platform generator, the imbalance power between the power that all generators send power and all load consumption can be expressed as:

$\mathrm{\Δ}{p}_{\mathrm{total}}={\mathrm{\Σ}}_{i=1}^N\mathrm{\Δ}{p}_i={\mathrm{\Σ}}_{i=1}^N\frac{2H_i}{f_n}\frac{{\mathrm{df}}_i}{\mathrm{dt}}=\frac{2H_{\mathrm{ec}}}{f_n}\frac{{\mathrm{df}}_{\mathrm{ec}}}{\mathrm{dt}}---\left(6\right)$

Wherein,

$H_{\mathrm{ec}}=\underset{i=1}{\overset{N}{\mathrm{\Σ}}}{H}_i$

$f_{\mathrm{ec}}=\underset{i=1}{\overset{N}{\mathrm{\Σ}}}{H}_i{f}_i/\underset{i=1}{\overset{N}{\mathrm{\Σ}}}{H}_i$

H _ecand f _ecthe inertia centre frequency of equivalent inertia constant and equivalence respectively.

In micro-capacitance sensor, do not need the frequency f at computing system equivalent inertia center _ec, directly can calculate the frequency of micro-grid connection point bus, this is because micro-capacitance sensor is general all in the very near comparatively zonule of electrical distance, frequency departure is little.In addition, the quick calculating of Δ P is so also conducive to.

Therefore, the perturbation amplitude in power distribution network is estimated to be reduced to:

$\mathrm{\Δ}{P}_{\mathrm{total}}=\frac{2H_{\mathrm{ec}}}{f_n}\·\frac{{\mathrm{df}}_{\mathrm{meas}}}{\mathrm{dt}}=\frac{2}{f_n}(H_{\mathrm{SG}}+H_{\mathrm{WF}}+H_{\mathrm{PV}}+H_{\mathrm{battery}})\frac{{\mathrm{df}}_{\mathrm{meas}}}{\mathrm{dt}}---\left(8\right).$

2 utilize droop control to slow down frequency change

Equivalent inertia constant in micro-capacitance sensor is often less, although blower fan can be gone out " moment of inertia " by virtual rotation inertia simulation, but the frequency fluctuation in micro-capacitance sensor is still relatively violent, the pace of change of frequency can be slowed down by the droop control of energy storage and blower fan.Fig. 2 represents that the frequency response of energy-storage system controls, and energy storage is expressed as the inverter of a voltage source and DC/AC, uses the P-Q control mode shown in Fig. 3 and Fig. 4.

No matter how extraneous service conditions changes traditional energy-storage system, remains that the output of meritorious P and idle Q is constant.It system cloud gray model at unstable region or the state of emergency time, frequency stability is not contributed.But, when increasing various types of energy storage device (such as electric automobile, battery etc.) is integrated in system, the just necessary frequency stability by energy storage device improved system.Fig. 5 is exactly the frequency response real power control of energy-storage system, i.e. droop control, and this control uses system frequency as input signal, and when system frequency changes, battery changes power stage dynamically.

The P-Q of wind energy turbine set controls

Wind energy turbine set does not generally provide frequency to support, and when wind speed changes, blower fan adjusts its propeller pitch angle, catches maximum wind energy.After micro-capacitance sensor and external electrical network are isolated, need the frequency stability that F-P controls improved system is installed.

Fig. 6 gives the droop control block diagram of wind turbine, and droop control is using system frequency as input signal, and when system frequency changes, the reference moment of wind turbine also can dynamic change.

3 dynamic self-balance load sheddings

Initial power vacancy in micro-capacitance sensor, balances primarily of rotation function, adjustment effect of load and distributed power source regulating power three part.Rotation function only plays a role in the unbalanced Initial Dynamic Process of power, and final load variations is born by the regulating action of distributed power source and load.The inertia constant of distributed power source is usually very little, and self-balancing ability is very weak, and the micro-capacitance sensor frequency response models of simplification as shown in Figure 7.

The Load Regulation ability φ of micro-capacitance sensor:

φ＝ΔP _G-ΔP _L＝-K _GΔfP _Ge-K _LΔfP _Le＝-K _SΔfP _Ge(9)

Δ f--micro-capacitance sensor stablize after frequency departure, relevant with the adjustable range of primary frequency modulation, be generally less than 0.2Hz;

K _g--the power versus frequency characteristic coefficient of generator, turbo generator is generally 16.6-25;

K _l--LOAD FREQUENCY mediating effect+6 coefficient, generally between 1 and 3.

Ability and machine set type, governing system characteristic and the generating set of the primary frequency modulation situation such as subsequent use of exerting oneself is relevant.The fm capacity of turbo-generator is better than turbo generator, and the response of hydrogovernor then slowly.Compared with the regulating action of speed regulator, adjustment effect of load is much smaller, and in the micro-capacitance sensor having enough spinning reserves, adjustment effect of load can be ignored.When the power supply power that overfills is run namely for subsequent use without spin, Δ P _g=0, now only carry out balanced power vacancy by adjustment effect of load.

By step 1) computing system power difference, then calculate the power needing off-load,

$P_{\mathrm{shedding}}=\frac{{2H}_{\mathrm{ec}}}{f_n}\frac{{\mathrm{df}}_{\mathrm{meas}}}{\mathrm{dt}}-\mathrm{k\φ}---\left(10\right)$

Be wherein adjustment factor, span is generally 0 ~ 1.For reducing cutting load amount, generator regulating power and adjustment effect of load should be taken into full account, the value improved as far as possible.And for making micro-capacitance sensor return to rapidly rated frequency, then need cutting load amount as far as possible close to power shortage, the value namely reduced.

As k=1, system still can rest on low frequency state the long period, and recovery process is slow; During k=0, system will return to rated frequency very soon.In practical application, rational adjustment factor k should be determined according to the type of generator and governing system response characteristic.

Because the capacity of micro-capacitance sensor is little, equivalent inertia is little, less power shortage just may cause the degradation of frequency.In order to unnecessary load can be cut in time, stop the continuous decrease of frequency, need to obtain the requirement to the off-load time under various power shortage.System frequency dynamic characteristic when can obtain for subsequent use without spin, when deriving different capacity vacancy, frequency drops to the time of certain certain value, namely maximum off-load moment-power shortage (t-dP) curve.

$t_{\max }=-\frac{H}{K_L}\ln (1-\frac{\mathrm{\Δf}{K}_L}{f_n\mathrm{\ΔP}})---\left(11\right).$

As shown in Figure 8, curve a is H=10s, K _lmicro-capacitance sensor frequency when different capacity vacancy of=2 is down to the time graph of 47Hz.The corresponding power shortage percentage of curvilinear abscissa, the ordinate corresponding maximum off-load moment.Such as power shortage 80% time, unnecessary load must be taken measures before 0.8s to excise, otherwise frequency will lower than 47Hz.

Region 1 is the stagnant areas of curve a, and power shortage now can be balanced by load effect, and frequency can not decline further.Region 2 is the off-load region of curve a, and now power shortage is comparatively large, and frequency keeps declines.Region 3 is the breakdown region of curve a, and frequency drops to Minimum Acceptable Value, causes frequency collapse.When power shortage is very large, frequency decrease is very fast, and the available time is very short.General real-time load shedding should action in 0.5s after disturbance, though now 100% power shortage under, frequency also can not be reduced to 47Hz.

According to the t calculated _max, can determine the round n of off-load, each off-load amount is Δ P _i:

$\mathrm{\Δ}{P}_i=K_i\mathrm{\Δ}{P}_{\mathrm{total}}=K_i\frac{{2H}_{\mathrm{ec}}}{f_n}\·\frac{{\mathrm{df}}_{\mathrm{meas}}}{\mathrm{dt}}---\left(12\right)$

$\underset{i=1}{\overset{n}{\mathrm{\Σ}}}{K}_i=1---\left(13\right)$

Complete adjustment required time:

T＝n*t _a≤t _max(14)

When b, a are respectively underpower, frequency is down to the time graph of 49Hz, 48Hz, 47Hz.Power shortage is 0.3P _etime, when adopting traditional low-frequency off-load, micro-capacitance sensor frequency is down to 49Hz from e point through 0.72s, arrives x point.Now first round load shedding action, cuts away 0.1P _eload, operating point transits to f point.Now power shortage is 0.2P _e, drop to 48Hz through 1.44s (f → y) frequency, second takes turns load shedding action, and frequency starts to recover (y → z).The total time that load shedding consumes is t _{e → x}+ t _{f → y}.So when operating point is in region 2, needs to cut enough loads before arrival region 3, make operating point enter region 1.When adopting real-time load shedding, before frequency not yet arrives x point, complete off-load, operating point directly transits in region 1.

By reference to the accompanying drawings the specific embodiment of the present invention is described although above-mentioned; but not limiting the scope of the invention; one of ordinary skill in the art should be understood that; on the basis of technical scheme of the present invention, those skilled in the art do not need to pay various amendment or distortion that creative work can make still within protection scope of the present invention.

Claims (10)

1. a micro-capacitance sensor self-balancing fast cutback control method, is characterized in that: comprise the following steps:

(1) determine the equivalent inertia constant of micro-capacitance sensor, calculate the power difference of micro-capacitance sensor, the perturbation amplitude in micro-capacitance sensor is estimated;

(2) pace of change of frequency is slowed down respectively by the frequency response real power control of energy-storage system and the droop control of wind turbine;

(3) utilize rotation function, adjustment effect of load and distributed power source regulating power to balance the initial power vacancy in micro-capacitance sensor, according to power difference, calculate the power needing off-load, by under various power shortage to the requirement of off-load time, system frequency dynamic characteristic when obtaining for subsequent use without spin.

2. a kind of micro-capacitance sensor self-balancing fast cutback control method as claimed in claim 1, is characterized in that: in described step (1), determines that the method for the equivalent inertia constant of micro-capacitance sensor is:

In the power distribution network comprising mixing generation of electricity by new energy, inertia constant H is defined by following formula:

But to dissimilar generator, the definition of inertia constant is different; The H of synchronous generator _sG

Wherein, J is the moment of inertia kgm of rotor ², ω _mthe rated speed rad/s of rotor,

For constant speed deliberate speed loss wind field:

J is the moment of inertia of every Fans, and N is the blower fan number of units put into operation; ω _miit is the speed of service of blower fan i.

3. a kind of micro-capacitance sensor self-balancing fast cutback control method as claimed in claim 2, it is characterized in that: in described step (1), when wind energy turbine set, photovoltaic plant and energy storage device are by back-to-back converters connecting system, they are 0 to the equivalent inertia constant of systematic contributions, if but addition of the blower fan of virtual inertia simulation link, photovoltaic plant and energy storage device, then its equivalent inertia constant non-vanishing; Now need the equivalent inertia constant of On-line Estimation distributed power source.

4. a kind of micro-capacitance sensor self-balancing fast cutback control method as claimed in claim 3, it is characterized in that: in described step (1), the sub-controller installed by the outlet of each distributed power source gathers power and the frequency change rate of distributed power source outlet, estimate the equivalent inertia constant of this distributed power source, the data window length that order calculates is N ₁* t _s, wherein N ₁for sampled point number, t _sfor the sampling interval; Data window interval length gets N ₂* t _s, then the inertia estimated is:

${2H}_{\mathrm{eq}}=-\frac{{\stackrel{\‾}{P}}_{\mathrm{pos}}-{\stackrel{\‾}{P}}_{\mathrm{pre}}}{\stackrel{\‾}{R}{\stackrel{\‾}{F}}_{\mathrm{pos}}-\stackrel{\‾}{R}{\stackrel{\‾}{F}}_{\mathrm{pre}}}---\left(4\right)$

${\stackrel{\‾}{P}}_{\mathrm{pre}}=\frac{1}{N_1}\underset{i=1}{\overset{N_1}{\mathrm{\Σ}}}P\left(i\right),{\stackrel{\‾}{P}}_{\mathrm{pos}}=\frac{1}{N_1}\underset{i=N_1+N_2+1}{\overset{{2N}_1+N_2}{\mathrm{\Σ}}}P\left(i\right)$

${\stackrel{\‾}{R}\stackrel{\‾}{P}}_{\mathrm{pre}}=\frac{1}{N_1}\underset{i=1}{\overset{N_1}{\mathrm{\Σ}}}\frac{\mathrm{df}}{\mathrm{dt}}\left(i\right),{\stackrel{\‾}{R}\stackrel{\‾}{P}}_{\mathrm{pos}}=\frac{1}{N_1}\underset{i=N_1+N_2+1}{\overset{{2N}_1+N_2}{\mathrm{\Σ}}}\frac{\mathrm{df}}{\mathrm{dt}}\left(i\right)$

Wherein, P (i) and for distributed power source exports power and the frequency change rate sampled value of i-th sampled point, N ₁and N ₂span is 3-5; Disturbance each time in micro-capacitance sensor, sub-controller all will estimate the equivalent inertia constant of distributed power source, and this estimated value is uploaded to micro-grid master control device.

5. a kind of micro-capacitance sensor self-balancing fast cutback control method as claimed in claim 1, it is characterized in that: in described step (1), the perturbation amplitude method of estimation in micro-capacitance sensor is: the inertia constant H establishing every platform generator _i, record frequency change rate at generator port obtain the imbalance power Δ p of generator port _i, i.e. generator mechanical power p _miwith electromagnetic power p _eibetween imbalance power:

$\frac{{2H}_i}{f_n}\frac{{\mathrm{df}}_i}{\mathrm{dt}}=p_{\mathrm{mi}}-p_{\mathrm{ei}}=\mathrm{\Δ}{p}_i---\left(5\right)$

Wherein, f _nfor electrical network rated frequency; In the system with N platform generator, the imbalance power between the power that all generators send power and all load consumption is expressed as:

${\mathrm{\Δp}}_{\mathrm{total}}={\mathrm{\Σ}}_{i=1}^N{\mathrm{\Δp}}_i={\mathrm{\Σ}}_{i=1}^N\frac{{2H}_i}{f_n}\frac{{\mathrm{df}}_i}{\mathrm{dt}}=\frac{{2H}_{\mathrm{ec}}}{f_n}\frac{{\mathrm{df}}_{\mathrm{ec}}}{\mathrm{dt}}---\left(6\right)$

Wherein,

$H_{\mathrm{ec}}=\underset{i=1}{\overset{N}{\mathrm{\Σ}}}{H}_i$

$f_{\mathrm{ec}}=\underset{i=1}{\overset{N}{\mathrm{\Σ}}}{H}_i{f}_i/\underset{i=1}{\overset{N}{\mathrm{\Σ}}}{H}_i$

H _ecand f _ecthe inertia centre frequency of equivalent inertia constant and equivalence respectively.

6. a kind of micro-capacitance sensor self-balancing fast cutback control method as claimed in claim 1, is characterized in that: in described step (1), in micro-capacitance sensor, ignore the frequency f at computing system equivalent inertia center _ec, directly calculate the frequency f of micro-grid connection point bus _meas, the perturbation amplitude simplified in power distribution network is estimated as:

${\mathrm{\ΔP}}_{\mathrm{total}}=\frac{{2H}_{\mathrm{ec}}}{f_n}\·\frac{{\mathrm{df}}_{\mathrm{meas}}}{\mathrm{dt}}=\frac{2}{f_n}(H_{\mathrm{SG}}+H_{\mathrm{WF}}+H_{\mathrm{PV}}+H_{\mathrm{battery}})\frac{{\mathrm{df}}_{\mathrm{meas}}}{\mathrm{dt}}---\left(8\right);$

Wherein, H _pVand H _batterybe respectively the equivalent inertia constant of photovoltaic plant and battery.

7. a kind of micro-capacitance sensor self-balancing fast cutback control method as claimed in claim 1, it is characterized in that: in described step (2), the frequency response real power control of energy-storage system, namely droop control method is: use system frequency as input signal, when system frequency changes, battery changes power stage dynamically;

In described step (2), the droop control method of wind turbine is: using system frequency as input signal, when system frequency changes, and the reference moment dynamic change thereupon of wind turbine.

8. a kind of micro-capacitance sensor self-balancing fast cutback control method as claimed in claim 1, is characterized in that: in described step (3), by computing system power difference, then calculate the power needing off-load,

$P_{\mathrm{shedding}}=\frac{{2H}_{\mathrm{ec}}}{f_n}\frac{{\mathrm{df}}_{\mathrm{meas}}}{\mathrm{dt}}-\mathrm{k\φ}---\left(10\right)$

Wherein k is adjustment factor, and span is 0 ~ 1;

Wherein, the Load Regulation ability φ of micro-capacitance sensor:

φ＝ΔP _G-ΔP _L＝-K _GΔfP _Ge-K _LΔfP _Le＝-K _SΔfP _Ge(9)

Δ f--micro-capacitance sensor stablize after frequency departure, relevant with the adjustable range of primary frequency modulation;

K _g--the power versus frequency characteristic coefficient of generator;

K _l--LOAD FREQUENCY mediating effect+6 coefficient;

P _ge, P _le--the rated generation power of micro-capacitance sensor and rated load power.

9. a kind of micro-capacitance sensor self-balancing fast cutback control method as claimed in claim 1, is characterized in that: in described step (3), for reducing cutting load amount, should take into full account generator regulating power and adjustment effect of load, the value improved as far as possible; And for making micro-capacitance sensor return to rapidly rated frequency, then need cutting load amount as far as possible close to power shortage, the value namely reduced; As k=1, system still can rest on low frequency state the long period, and recovery process is slow; During k=0, system will return to rated frequency very soon; Rational adjustment factor k is determined according to the type of generator and governing system response characteristic.

10. a kind of micro-capacitance sensor self-balancing fast cutback control method as claimed in claim 9, it is characterized in that: in described step (3), in order to unnecessary load can be cut in time, stop the continuous decrease of frequency, need to obtain the requirement to the off-load time under various power shortage; System frequency dynamic characteristic when obtaining for subsequent use without spin, when deriving different capacity vacancy, frequency drops to the time of certain certain value, namely maximum off-load moment-power shortage (t-dP) curve:

$t_{\max }=-\frac{H}{K_L}\ln (1-\frac{{\mathrm{\ΔfK}}_L}{f_n\mathrm{\ΔP}})---\left(11\right).$

According to the t calculated _max, determine the round n of off-load, each off-load amount is Δ P _i:

${\mathrm{\ΔP}}_i=K_i{\mathrm{\ΔP}}_{\mathrm{total}}=K_i\frac{{2H}_{\mathrm{ec}}}{f_n}\·\frac{{\mathrm{df}}_{\mathrm{meas}}}{\mathrm{dt}}---\left(12\right);$

$\underset{i=1}{\overset{n}{\mathrm{\Σ}}}{K}_i=1---\left(13\right);$

Complete adjustment required time:

T＝n*t _a≤t _max(14)。